most citedSpectralGPT: Spectral Remote Sensing Foundation Model

838 citations · 858 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2025

KANO: Kolmogorov-Arnold Neural Operator for Image Super-Resolution

Chenyu Li, Danfeng Hong, Bing Zhang +2

The highly nonlinear degradation process, complex physical interactions, and various sources of uncertainty render single-image Super-resolution (SR) a particularly challenging tas…

cs.CV2025

MambaX: Image Super-Resolution with State Predictive Control

Chenyu Li, Danfeng Hong, Bing Zhang +3

Image super-resolution (SR) is a critical technology for overcoming the inherent hardware limitations of sensors. However, existing approaches mainly focus on directly enhancing th…

cs.CV2025★ 4 cited

Hyperspectral Imaging

Danfeng Hong, Chenyu Li, Naoto Yokoya +6

Hyperspectral imaging (HSI) is an advanced sensing modality that simultaneously captures spatial and spectral information, enabling non-invasive, label-free analysis of material, c…

cs.CV2025★ 8 cited

UrbanSAM: Learning Invariance-Inspired Adapters for Segment Anything Models in Urban Construction

Chenyu Li, Danfeng Hong, Bing Zhang +4

Object extraction and segmentation from remote sensing (RS) images is a critical yet challenging task in urban environment monitoring. Urban morphology is inherently complex, with…

cs.CV2023★ 838 cited

SpectralGPT: Spectral Remote Sensing Foundation Model

Danfeng Hong, Bing Zhang, Xuyang Li +11

The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. Whil…

cs.CV2023★ 8 cited

Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks

Danfeng Hong, Bing Zhang, Hao Li +7

Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-modality-dominated remote sensing (RS) applications, especially with an emphasis on indivi…